Document layout analysis is a crucial prerequisite for document
understanding, including document retrieval and conversion. Most public
datasets currently contain only PDF documents and lack realistic documents.
Models trained on these datasets may not generalize well to real-world
scenarios. Therefore, this paper introduces a large and diverse document layout
analysis dataset called $M^{6}Doc$. The $M^6$ designation represents six
properties: (1) Multi-Format (including scanned, photographed, and PDF
documents); (2) Multi-Type (such as scientific articles, textbooks, books, test
papers, magazines, newspapers, and notes); (3) Multi-Layout (rectangular,
Manhattan, non-Manhattan, and multi-column Manhattan); (4) Multi-Language
(Chinese and English); (5) Multi-Annotation Category (74 types of annotation
labels with 237,116 annotation instances in 9,080 manually annotated pages);
and (6) Modern documents. Additionally, we propose a transformer-based document
layout analysis method called TransDLANet, which leverages an adaptive element
matching mechanism that enables query embedding to better match ground truth to
improve recall, and constructs a segmentation branch for more precise document
image instance segmentation. We conduct a comprehensive evaluation of
$M^{6}Doc$ with various layout analysis methods and demonstrate its
effectiveness. TransDLANet achieves state-of-the-art performance on $M^{6}Doc$
with 64.5\% mAP. The $M^{6}Doc$ dataset will be available at
this https URL

本文介绍了一种名为 $M^{6} Doc$ 的大型多样化文档布局分析数据集和一种基于 transformer 的文档布局分析方法 TransDLANet。该方法采用自适应元素匹配机制和构建分割分支，以更好地匹配真实情况，并且实现更精确的文档图像实例分割，其中 $M^{6} Doc$ 数据集展示了其有效性，并且 TransDLANet 在该数据集上达到了 64.5％的 mAP，该数据集具有多种注释类别的特点。

M$^{6}$Doc：现代文档布局分析的大规模多格式、多类型、多布局、多语言、多注释类别数据集

M$^{6}$Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout,  Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout  Analysis